A NASA–Air Force Precipitation Analysis for Near-Real-Time Operations

Abstract

Abstract This article describes a new precipitation analysis algorithm developed by NASA for time-sensitive operations at the United States Air Force. Implemented as part of the Land Information System—a land modeling and data assimilation software framework—this NASA–Air Force Precipitation Analysis (NAFPA) combines numerical weather prediction model outputs with rain gauge measurements and satellite estimates to produce global, gridded 3-h accumulated precipitation fields at approximately 10-km resolution. Input observations are subjected to quality control checks before being used by the Bratseth analysis algorithm that converges to optimal interpolation. NAFPA assimilates up to 3.5 million observations without artificial data thinning or selection. To evaluate this new approach, a multiyear reanalysis is generated and intercompared with eight alternative precipitation products across the contiguous United States, Africa, and the monsoon region of eastern Asia. NAFPA yields superior accuracy and correlation over low-latency (up to 14 h) alternatives (numerical weather prediction and satellite retrievals), and often outperforms high-latency (up to 3.5 months) products, although the details for the latter vary by region and product. The development of NAFPA offers a high-quality, near-real-time product for use in meteorological, land surface, and hydrological research and applications. Significance Statement Precipitation is a key input to land modeling systems due to effects on soil moisture and other parts of the hydrologic cycle. It is also of interest to government decision-makers due to impacts on human activities. Here we present a new precipitation analysis based on available near-real-time data. By running the program for prior years and comparing with alternative products, we demonstrate that our analysis provides better accuracy and usually less bias than near-real-time satellite data alone, and better accuracy and correlation than data provided by numerical weather models. Our analysis is also competitive with other products created months after the fact, justifying confidence in using our analysis in near-real-time operations.